Building automation systems are centralized, interlinked, networks of hardware and software, which monitor and control the environmental conditions in an area of a building or a whole building such as commercial, industrial, institutional facilities, etc. For example, a Heating, Ventilation and Air Conditioning (HVAC) control system is used for regulating environmental condition of a building such as temperature. Generally, an environmental condition, for example a temperature, is sought to be obtained. For that purpose, a control system may implement a Proportional-Integral-Derivative (PID) algorithm for further drawing a conclusion on an action to be taken, such as heating, cooling, ventilating etc. Such a control system is referred to as a PID controller.
Although efficient, the PID controller often requires a rather complex calibration to obtain the proper degree of efficiency. Furthermore, a poorly or inappropriately calibrated PID controller provides an average control and efficiency. Also, the derivative of the error amplifies higher frequency measurements, which sometimes distort an output of the control system, resulting in a poor environmental condition control. Moreover, the PID controller usually has a linear output, which is highly inappropriate for non-linear systems such as HVAC control systems.
One way to improve the control provided by the PID controller of an environmental condition in an area of a building is to use an adaptive control based on recursive calculations to control the environmental condition. For doing so, an environmental conditional adjustment value (yn) is recursively calculated, as will be detailed later in the description. The recursive calculation of the conditional adjustment value (yn) is based on several parameters, and different recursive functions may be used for implementing the recursive calculation. However, a given PID controller may implement only one of the recursive functions, while the current environment condition would be better addressed with another one of the recursive functions. Alternatively, a given PID controller implements several of the recursive function; but is not always capable of selecting which one among the implemented recursive functions will provide the best control for a given environmental condition.
Current advances in artificial intelligence, and more specifically in neural networks, can be taken advantage of to replace all the recursive functions. More specifically, a model taking into consideration the parameters of the recursive functions to determine the conditional adjustment value (yn) can be generated and used by a neural network.
Therefore, there is a need for a controller comprising an adaptive control based on a neural network, and for a method and computer program product, for controlling an environmental condition in an area of a building using the neural network based adaptive control.